Issue 21, 2023

A data-driven sequencer that unveils latent “codons” in synthetic copolymers

Abstract

The recent emergence of sequence engineering in synthetic copolymers has been innovating polymer materials, where short sequences, hereinafter called “codons” using an analogy from nucleotide triads, play key roles in expressing functions. However, the codon compositions cannot be experimentally determined owing to the lack of efficient sequencing methods, hindering the integration of experiments and theories. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. Despite the random fragmentation along copolymer main-chains, the characteristic fragment patterns of the codons are identified and quantified via unsupervised learning of a spectral dataset of random copolymers. The codon complexities increase with their length and monomer component number. Our data-driven approach accommodates the increasing complexities by expanding the dataset; the codon compositions of binary triads, binary pentads and ternary triads are quantifiable with small datasets (N < 100). The sequencer allows describing copolymers with their codon compositions/distributions, facilitating sequence engineering toward innovative polymer materials.

Graphical abstract: A data-driven sequencer that unveils latent “codons” in synthetic copolymers

Supplementary files

Article information

Article type
Edge Article
Submitted
20 dec 2022
Accepted
19 mar 2023
First published
20 mar 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2023,14, 5619-5626

A data-driven sequencer that unveils latent “codons” in synthetic copolymers

Y. Hibi, S. Uesaka and M. Naito, Chem. Sci., 2023, 14, 5619 DOI: 10.1039/D2SC06974A

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